You will get Build Generative AI RAG with LLM for Chatbots & Automation
Project details
You will get a production-ready Generative AI solution built using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to deliver accurate, context-aware, and verifiable AI responses.
Unlike generic AI setups, this project focuses on reducing hallucinations, improving response relevance, and ensuring your AI system uses your own data (documents, websites, or databases). I design scalable pipelines suitable for chatbots, internal knowledge assistants, document Q&A, and AI-powered content generation.
With hands-on experience building real-world AI systems, I follow a structured workflow including data ingestion, vector indexing, prompt engineering, evaluation, and optimization. You receive clean, well-documented code, performance insights, and optional deployment guidance.
Whether you are a startup validating an AI idea or a business deploying a reliable AI assistant, this project delivers trustworthy, enterprise-ready AI tailored to your needs.
Unlike generic AI setups, this project focuses on reducing hallucinations, improving response relevance, and ensuring your AI system uses your own data (documents, websites, or databases). I design scalable pipelines suitable for chatbots, internal knowledge assistants, document Q&A, and AI-powered content generation.
With hands-on experience building real-world AI systems, I follow a structured workflow including data ingestion, vector indexing, prompt engineering, evaluation, and optimization. You receive clean, well-documented code, performance insights, and optional deployment guidance.
Whether you are a startup validating an AI idea or a business deploying a reliable AI assistant, this project delivers trustworthy, enterprise-ready AI tailored to your needs.
AI Algorithms
Autoencoder, CycleGAN, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer Model, Variational AutoencoderAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, AI-Generated Art, AI-Generated Code, AI-Generated Music, AI-Generated Video, AIOps, Automatic Speech Recognition, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, Microsoft 365 Copilot, Microsoft CNTK, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
BERT, BLOOM, DALL-E, GPT-3, GPT-4, GPT-J, LLaMA, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$299
|
Standard
$649
|
Advanced
$1,199
|
|---|---|---|---|
| Delivery Time | 3 days | 6 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | ||
Database Integration | - | - | |
Detailed Code Comments | |||
Image Upscaling | - | - | |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | - | ||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | ||
Natural Language Processing | - | ||
NLP Tokenization | - | - | |
Pre-Training | - | - | |
Prompt Engineering | - | ||
Setup File | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $320
Deployment Support
(+ 2 Days)
+$200
Extended Model Evaluation
(+ 2 Days)
+$150
🔸 Technical Documentation (Client-Ready)
(+ 1 Day)
+$120Frequently asked questions
About Muhammad
AI & ML Engineer | Production-Ready ML Systems | Deep Learning
Lahore, Pakistan - 1:25 pm local time
As an experienced AI / Machine Learning Engineer, I specialize in building end-to-end intelligent systems; from data engineering and model development to deployment, monitoring, and optimization. I work with startups and enterprises to design scalable, explainable, and reliable AI solutions, not just experiments or research prototypes.
🧠 Core Expertise
🤖 Machine Learning & AI Development
• Custom ML / Deep Learning model development and optimization
• Predictive analytics and demand forecasting systems
• Risk modeling and intelligent optimization algorithms
• Production-grade AI system architecture and ML pipelines
📊 Data Engineering & Science
• Advanced feature engineering and data preprocessing
• Statistical analysis and exploratory data analysis (EDA)
• Time-series forecasting and anomaly detection
• A/B testing frameworks and experimentation design
🚀 Deployment, MLOps & Infrastructure
• AI-powered REST APIs and microservices
• Model deployment using Docker and Kubernetes
• Cloud infrastructure: AWS, GCP, Azure
• MLOps: monitoring, logging, explainability, and lifecycle management
• Inference optimization and performance tuning
🛠️ Technology Stack
Languages & Frameworks
Python, SQL, NumPy, Pandas, Scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, TensorFlow, Keras
Data & Storage
PostgreSQL, MySQL, Redis, Feature Stores, Data Warehousing
APIs & Services
FastAPI, Flask, RESTful APIs
DevOps & Cloud
Docker, Kubernetes, CI/CD pipelines, AWS, GCP, Azure, Model Versioning
ML Tooling
Jupyter, MLflow, Weights & Biases, Hyperparameter Tuning, AutoML
✅ My Approach
• ✅ Problem-first thinking — business goals before models
• ✅ Explainable AI — interpretable systems stakeholders trust
• ✅ Clean architecture — scalable, maintainable code
• ✅ Production mindset — reliability, performance, long-term stability
• ✅ Clear communication — regular updates and collaboration
🔬 Advanced Research & Niche Expertise
● Hybrid Quantum-AI systems, QUBO optimization models, quantum-inspired algorithms, and applied research in quantum machine learning for complex optimization problems.
● Quantum Machine Learning (QML): Practical algorithm development using Variational Quantum Circuits (VQC), VQE, QLSTM and QAOA via Pennylane or Qiskit for optimization and simulation problems.
📩 Let’s discuss how I can help you build AI systems that create a real competitive advantage.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Use Case & Data Analysis
Review requirements, analyze data sources, and finalize system architecture.
RAG Pipeline Design
Set up vector database, document ingestion, embedding, and retrieval logic.